CVE-2020-15197: TensorFlow-GPU SparseCountSparseOutput Rank Validation Failure
Summary
CVE-2020-15197: In Tensorflow before version 2.3.1, the SparseCountSparseOutput implementation does not validate that the input arguments form a valid sparse tensor. Affects tensorflow-gpu across 2 version ranges.
< 2.3.1
< 3cbb917b4714766030b28eba9fb41bb97ce9ee02
Fixed in 3cbb917b4714766030b28eba9fb41bb97ce9ee02. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow-cpu · PyPI
- tensorflow · PyPI
The same CVE-2020-15197 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.
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Background
TensorFlow-GPU is an open-source machine learning framework developed by Google. It provides a comprehensive ecosystem of tools, libraries, and resources for building and deploying machine learning models. TensorFlow-GPU leverages the power of GPUs to accelerate the training and inference processes, making it suitable for high-performance computing environments. However, a vulnerability has been identified in versions prior to 2.3.1 that affects the SparseCountSparseOutput implementation. This vulnerability allows malicious users to pass tensors of different ranks, leading to a CHECK assertion failure and a potential denial of service attack. The issue has been patched in TensorFlow version 2.3.1.
Vulnerability Detail
The vulnerability in TensorFlow-GPU's SparseCountSparseOutput implementation arises from the lack of validation for the input arguments forming a valid sparse tensor. Specifically, the indices tensor, which must have a rank of 2 to be accessed as a matrix, is not properly validated. This oversight enables malicious users to provide tensors of different ranks, causing a CHECK assertion failure and a subsequent crash. Exploiting this vulnerability can result in denial of service in serving installations, especially if users have control over the input sparse tensor components.
How To Fix
Latest published version: 2.12.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-gpu==2.12.0"Upgrading to it may include breaking changes; the version below is the minimum fixed release for this vulnerability.
If you need the minimum change
3cbb917b4714766030b28eba9fb41bb97ce9ee02
This is the first release that contains the fix.
pip install --upgrade "tensorflow-gpu==3cbb917b4714766030b28eba9fb41bb97ce9ee02"Tip — Not sure which version your app runs? Vulert can match this fix to it. Scan free →
Workaround
There is no known workaround for this vulnerability. It is essential to update to the patched version of TensorFlow-GPU to mitigate the risk of exploitation.
Conclusion
The vulnerability in TensorFlow-GPU's SparseCountSparseOutput implementation can be mitigated by updating to version 2.3.1 or later. It is crucial for users to promptly apply the patch to prevent potential denial of service attacks. For further assistance or support in vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-gpu →
FAQ
TensorFlow-GPU is an open-source machine learning framework developed by Google that leverages GPUs for accelerated training and inference processes.
SparseCountSparseOutput is a function in TensorFlow-GPU that counts the number of non-zero elements in a sparse tensor.
A sparse tensor is a multidimensional array that contains mostly zero values. It is used to efficiently represent and process data with a significant number of zeros.
A CHECK assertion failure occurs when a condition specified by the programmer is not met during program execution. In this case, the failure happens when the input arguments to SparseCountSparseOutput do not form a valid sparse tensor.
This vulnerability can be exploited by malicious users to cause denial of service in serving installations of TensorFlow-GPU. By providing tensors of different ranks, they can trigger a CHECK assertion failure and crash the system.
References
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